{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Extracting_features_Material_data.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPm//ORk/e63Qm86tm7N9h7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MathieuGrosso/projet-dima-/blob/main/Extracting_features_Material_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERiunpCMsF7X",
        "outputId": "46bd1928-bc13-4239-86ee-1095d3ad6829"
      },
      "source": [
        "pip install pymicro"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pymicro in /usr/local/lib/python3.7/dist-packages (0.5.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMXLQNB_t0hW"
      },
      "source": [
        ""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-QqBiJWOFBS"
      },
      "source": [
        "import sys\n",
        "import os\n",
        "import functools\n",
        "import pathlib\n",
        "import pickle\n",
        "from google.colab import drive\n",
        "import functools\n",
        "import pathlib\n",
        "import pickle\n",
        "\n",
        "import numpy as np\n",
        "import pymicro\n",
        "from pymicro.file import file_utils\n",
        "import IPython\n",
        "from IPython.display import clear_output\n",
        "import matplotlib\n",
        "from matplotlib import pyplot as plt, cm\n",
        "import numpy as np \n",
        "\n",
        "from PIL import Image\n",
        "from array import *\n",
        "from random import shuffle\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "from matplotlib import pyplot as plt, cm\n",
        "\n",
        "import seaborn as sns\n",
        "from sklearn.manifold import TSNE, Isomap, MDS\n",
        "from sklearn.decomposition import PCA\n",
        "from tensorflow.keras.layers import Input, Dense, Lambda, Flatten, Reshape\n",
        "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, BatchNormalization\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras import metrics\n",
        "from tensorflow.keras.datasets import mnist, fashion_mnist\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.metrics import roc_curve,auc,roc_auc_score"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWNjOHMgS9hb",
        "outputId": "5ad699d8-be9e-466a-bd98-06427da23918"
      },
      "source": [
        "print(sys.version)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.7.10 (default, Feb 20 2021, 21:17:23) \n",
            "[GCC 7.5.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xCrkHzBgqr1V"
      },
      "source": [
        "#Save Data, Load them, and crops them"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSMpE9_c9WE0"
      },
      "source": [
        "## Crops the data for anomaly detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6Ssp8z6quMs",
        "outputId": "de09e907-dbad-4c2a-d1d3-26c43cdfbb5e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "!ls"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n",
            "drive  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Xukiyp6duzL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71ac1384-eeda-439a-83a8-7f4c6206e85b"
      },
      "source": [
        "#Obtenir les images normales: \n",
        "%cd /content/drive/MyDrive/DIMA /Datasets\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/DIMA /Datasets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DUUia8Yqdya"
      },
      "source": [
        "data_dir = pathlib.Path('/content/drive/MyDrive/DIMA /Datasets/Data_big_raw') #changer la data_dir #grosse images\n",
        "#data_dir=pathlib.Path('/content/drive/MyDrive/DIMA /Datasets/Data_reduced') #petites images\n",
        "data_filepath = data_dir / \"pa66.raw\"\n",
        "labels_filepath = data_dir / \"pa66.ground_truth.raw\"\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "mHU_rw7lr7ib",
        "outputId": "c58684cf-0eac-41a3-9003-020a24b523ec"
      },
      "source": [
        "#ouvrir les fichiers raw: \n",
        "import os\n",
        "from pathlib import Path\n",
        "\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "src = list(files.upload().values())[0] \n",
        "open('read_raw.py','wb').write(src) \n",
        "\n",
        "from read_raw import HST_read\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a8dccb80-5073-4528-8b04-188e60f82946\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a8dccb80-5073-4528-8b04-188e60f82946\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving read_raw.py to read_raw (2).py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6X_hqDgFr2Yg"
      },
      "source": [
        "# this is just a trick to give names to these values\n",
        "# and put them together\n",
        "class label:\n",
        "    matrix = 0\n",
        "    fiber = 1\n",
        "    porosity = 2\n",
        "\n",
        "labels_list = [0, 1, 2]\n",
        "\n",
        "dimensions = (1300, 1040, 1900)\n",
        "dtype = \"uint8\"\n",
        "\n",
        "# prefill the function `HST_read`\n",
        "hst_read = functools.partial(\n",
        "    file_utils.HST_read,\n",
        "    autoparse_filename=False,  # the file names are not properly formatted\n",
        "    data_type=dtype,\n",
        "    dims=dimensions,\n",
        "    verbose=True,)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x0e5m3YBsR9x",
        "outputId": "0def9ff5-fcf8-469f-ae68-d9efd5b29759"
      },
      "source": [
        "data = hst_read(data_filepath)\n",
        "labels = hst_read(labels_filepath)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data type is uint8\n",
            "volume size is 1300 x 1040 x 1900\n",
            "reading volume... from byte 0\n",
            "data type is uint8\n",
            "volume size is 1300 x 1040 x 1900\n",
            "reading volume... from byte 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iS12sJLVr231"
      },
      "source": [
        "assert data.shape == labels.shape"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j55aqRRQqf3o"
      },
      "source": [
        "random_state = np.random.RandomState(42)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJiRdC_MqbaX"
      },
      "source": [
        "crop_size = 128  # 128 x 128 pixels square\n",
        "n_crops = 1000\n",
        "volume_shape = data.shape\n",
        "axes_limits = (\n",
        "    # x and y are smaller because i will get a random\n",
        "    # coordinate of the left most and upper most pixel of the crop\n",
        "    (0, volume_shape[0] - crop_size),  # x-axis\n",
        "    (0, volume_shape[1] - crop_size),  # y-axis\n",
        "    (0, volume_shape[2]),  # z-axis\n",
        ")\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIAFDyaCs50-"
      },
      "source": [
        "### crops of data without porosity : \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AUYnotRtBqj"
      },
      "source": [
        "def does_not_have_porosity(label_crop_: np.ndarray) -> bool:\n",
        "    return np.all(label_crop_ != label.porosity)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5h9OiBotJVH"
      },
      "source": [
        "condition = does_not_have_porosity"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7G8FZEatK12",
        "outputId": "2fda8981-776f-4dd3-ff11-dc26109ee55a"
      },
      "source": [
        "print(f\"found: 0\")\n",
        "crops = []\n",
        "\n",
        "while len(crops) < n_crops:\n",
        "    z = random_state.randint(*axes_limits[2])  # min / max in z\n",
        "    x0 = random_state.randint(*axes_limits[0])\n",
        "    y0 = random_state.randint(*axes_limits[1])\n",
        "    x1, y1 = x0 + crop_size, y0 + crop_size\n",
        "    \n",
        "    labels_crop = labels[x0:x1, y0:y1, z]\n",
        "    data_crop = data[x0:x1, y0:y1, z]\n",
        "    \n",
        "    if condition(labels_crop):\n",
        "        crops.append({\n",
        "            \"data\": data_crop.copy(),\n",
        "            \"labels\": labels_crop.copy(),\n",
        "        })\n",
        "        clear_output()\n",
        "        print(f\"found: {len(crops)}\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "found: 1000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJcZtYU_tM7k"
      },
      "source": [
        "without_porosity = crops"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFnJv77FtPmT"
      },
      "source": [
        "##crops of data with porosity: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38V5bLKctSAb"
      },
      "source": [
        "def has_porosity(label_crop_: np.ndarray) -> bool:\n",
        "    return np.any(label_crop_ == label.porosity)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJL2NjKTtMQ3"
      },
      "source": [
        "condition = has_porosity"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fMj7if-ttVUl",
        "outputId": "cb550615-1ae0-4147-de53-324642fc44cf"
      },
      "source": [
        "print(f\"found: 0\")\n",
        "crops = []\n",
        "\n",
        "while len(crops) < n_crops:\n",
        "    z = random_state.randint(*axes_limits[2])  # min / max in z\n",
        "    x0 = random_state.randint(*axes_limits[0])\n",
        "    y0 = random_state.randint(*axes_limits[1])\n",
        "    x1, y1 = x0 + crop_size, y0 + crop_size\n",
        "    \n",
        "    labels_crop = labels[x0:x1, y0:y1, z]\n",
        "    data_crop = data[x0:x1, y0:y1, z]\n",
        "    \n",
        "    if condition(labels_crop):\n",
        "        crops.append({\n",
        "            \"data\": data_crop.copy(),\n",
        "            \"labels\": labels_crop.copy(),\n",
        "        })\n",
        "        clear_output()\n",
        "        print(f\"found: {len(crops)}\")\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "found: 1000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygPJsxiktYX_"
      },
      "source": [
        "with_porosity = crops"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgQpIpSotgpO"
      },
      "source": [
        "## Save in drive and load them : \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3LSKe5DthFn",
        "outputId": "9cdbfc98-abee-4f8f-8aba-e047b265a60c"
      },
      "source": [
        "len(without_porosity), len(with_porosity)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 1000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzT8DIFntYwV"
      },
      "source": [
        "without_porosity_folder = (data_dir / f\"{crop_size}px-images-WITHOUT-porosity\").resolve()\n",
        "with_porosity_folder = (data_dir / f\"{crop_size}px-images-WITH-porosity\").resolve()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCCLUJ43tpL6"
      },
      "source": [
        "without_porosity_folder.mkdir(exist_ok=True)\n",
        "with_porosity_folder.mkdir(exist_ok=True)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTCrpIgQtrre"
      },
      "source": [
        "list_crops = without_porosity\n",
        "folder = without_porosity_folder"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UnsAWAQtsGC"
      },
      "source": [
        "for idx, crop in enumerate(list_crops):\n",
        "    data_png_filepath = (folder / f\"{idx:06d}-data.png\")\n",
        "    labels_png_filepath = (folder / f\"{idx:06d}-labels.png\")\n",
        "    crop_data = crop[\"data\"]\n",
        "    crop_labels = crop[\"labels\"]\n",
        "    matplotlib.image.imsave(data_png_filepath, crop_data, cmap=cm.gray, vmin=0, vmax=255)    \n",
        "    matplotlib.image.imsave(labels_png_filepath, crop_labels, cmap=cm.gray, vmin=0, vmax=2)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F598ddsWtwZ3"
      },
      "source": [
        "list_crops = with_porosity\n",
        "folder = with_porosity_folder"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGS-r1sTtwv0"
      },
      "source": [
        "for idx, crop in enumerate(list_crops):\n",
        "    data_png_filepath = (folder / f\"{idx:06d}-data.png\")\n",
        "    labels_png_filepath = (folder / f\"{idx:06d}-labels.png\")\n",
        "    crop_data = crop[\"data\"]\n",
        "    crop_labels = crop[\"labels\"]\n",
        "    matplotlib.image.imsave(data_png_filepath, crop_data, cmap=cm.gray, vmin=0, vmax=255)    \n",
        "    matplotlib.image.imsave(labels_png_filepath, crop_labels, cmap=cm.gray, vmin=0, vmax=2)    "
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tJAowkosxdX"
      },
      "source": [
        "# Preparation of the study: \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--lJKHgyxF4X"
      },
      "source": [
        "##build the data for the training and testing set: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYi4dqu7vA1P",
        "outputId": "a92f2348-0ceb-42d3-84bd-9cb657804785"
      },
      "source": [
        "#load google drive and the file\n",
        "drive.mount('/content/drive')\n",
        "os.chdir(\"/content/drive/MyDrive/DIMA /Datasets/Data_big_raw\")\n",
        "%cd /content/drive/MyDrive/DIMA /Datasets/Data_big_raw\n",
        "%ls"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content/drive/MyDrive/DIMA /Datasets\n",
            " \u001b[0m\u001b[01;34m128px-images-WITHOUT-porosity\u001b[0m/           \u001b[01;34mmodel2\u001b[0m/\n",
            " \u001b[01;34m128px-images-WITH-porosity\u001b[0m/              \u001b[01;34mmodel3\u001b[0m/\n",
            " dataset_anormal_origin_test_32_x.npy     \u001b[01;34mmodel4\u001b[0m/\n",
            " dataset_anormal_origin_test_32_y.npy     \u001b[01;34mmodel5\u001b[0m/\n",
            " dataset_anormal_rotated_test_32_x.npy    \u001b[01;34mmodel6\u001b[0m/\n",
            " dataset_anormal_rotated_test_32_y.npy    pa66.ground_truth.raw\n",
            " dataset_normal_deformed_train_32_x.npy   pa66.ground_truth.raw.info\n",
            " dataset_normal_deformed_train_32_y.npy   pa66.raw\n",
            " dataset_normal_origin_test_32_x.npy      pa66.raw.info\n",
            " dataset_normal_origin_test_32_y.npy      pa66.test.error_volume.raw\n",
            " dataset_normal_origin_train_32_x.npy     pa66.test.error_volume.raw.info\n",
            " dataset_normal_origin_train_32_y.npy     pa66.test.prediction.raw\n",
            " dataset_normal_rotated_test_32_x.npy     pa66.test.prediction.raw.info\n",
            " dataset_normal_rotated_test_32_y.npy     \u001b[01;34m__pycache__\u001b[0m/\n",
            " dataset_normal_rotated_train_32_x.npy   'read_raw (1).py'\n",
            " dataset_normal_rotated_train_32_y.npy   'read_raw (2).py'\n",
            " \u001b[01;34mmodel0\u001b[0m/                                  read_raw.py\n",
            " \u001b[01;34mmodel1\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-F1LEV3mo8JT"
      },
      "source": [
        "def create_data_set():\n",
        "  \n",
        "  data_dir = pathlib.Path(\".\").resolve()\n",
        "  file_list = os.listdir(data_dir)\n",
        "  file_list_train = {'x' : [], 'y' : []}\n",
        "  file_list_test = {'x' : [], 'y' : []}\n",
        "\n",
        "  for file_name in file_list:\n",
        "      if file_name[-4:] == '.npy':\n",
        "        key = file_name[-5]\n",
        "        if 'train' in file_name:\n",
        "          file_list_train[key].append(file_name)\n",
        "        elif 'test' in file_name:\n",
        "          file_list_test[key].append(file_name)\n",
        "  \n",
        "  train = {}\n",
        "  test = {}\n",
        "\n",
        "  for key in ['x', 'y']:\n",
        "    train_path = (data_dir / file_list_train[key][0]).resolve()\n",
        "    test_path = (data_dir / file_list_test[key][0]).resolve()\n",
        "    train_data = np.load(train_path)\n",
        "    test_data = np.load(test_path)\n",
        "    train[key] = train_data\n",
        "    test[key] = test_data\n",
        "\n",
        "  if len(file_list_train) > 1:\n",
        "    for key in ['x', 'y']:\n",
        "      for i in range(len(file_list_train[key]) - 1):\n",
        "        path = (data_dir / file_list_train[key][i+1]).resolve()\n",
        "        data = np.load(path)\n",
        "        train[key] = np.append(train[key], data, axis=0)\n",
        "\n",
        "  if len(file_list_test) > 1:\n",
        "    for key in ['x', 'y']:\n",
        "      for i in range(len(file_list_test[key]) - 1):\n",
        "        path = (data_dir / file_list_test[key][i+1]).resolve()\n",
        "        data = np.load(path)\n",
        "        test[key] = np.append(test[key], data, axis=0)\n",
        "\n",
        "  randnum1 = np.random.randint(0,100)\n",
        "  np.random.seed(randnum1)\n",
        "  np.random.shuffle(train['x'])\n",
        "  np.random.seed(randnum1)\n",
        "  np.random.shuffle(train['y'])\n",
        "  randnum2 = np.random.randint(0,100)\n",
        "  np.random.seed(randnum2)\n",
        "  np.random.shuffle(test['x'])\n",
        "  np.random.seed(randnum2)\n",
        "  np.random.shuffle(test['y'])\n",
        "\n",
        "  train['x'] = train['x'].astype('float32') / 255\n",
        "  test['x'] = test['x'].astype('float32') / 255\n",
        "  train['x'] = np.expand_dims(train['x'], axis=3)\n",
        "  test['x'] = np.expand_dims(test['x'], axis=3)\n",
        "\n",
        "  return (train['x'],train['y']),(test['x'],test['y'])"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "OAGOCBdQo--7",
        "outputId": "d04ed951-e82e-4a54-8cfb-c4c621e5d1ee"
      },
      "source": [
        "# Get the the training and the test dataset\n",
        "(x_train, y_train), (x_test, y_test) = create_data_set()\n",
        "\n",
        "print(type(x_train))\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(y_train[y_train==0].shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)\n",
        "print(y_test[y_test==0].shape)\n",
        "\n",
        "x_test_anormal = x_test[y_test==0]\n",
        "# Display some NORMAL images\n",
        "n = 10  # how many images we will display\n",
        "plt.figure(figsize=(20, 20))\n",
        "for i in range(n):\n",
        "    # display original\n",
        "    ax = plt.subplot(5, n, i + 1)\n",
        "    plt.imshow(x_train[i].reshape(32, 32))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(20, 20))\n",
        "for i in range(n):\n",
        "    # display original\n",
        "    ax = plt.subplot(5, n, i + 1)\n",
        "    plt.imshow(x_test_anormal[i].reshape(32, 32))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "(16800, 32, 32, 1)\n",
            "(16800,)\n",
            "(0,)\n",
            "(9600, 32, 32, 1)\n",
            "(9600,)\n",
            "(4800,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W5PAz6MKyJwJ"
      },
      "source": [
        "## function for creating the class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJ-YutS3ycPw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJ88cJe2yGMT"
      },
      "source": [
        "## Function for evaluating performance "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMh7Fs8XybSG"
      },
      "source": [
        "def construct_model():\n",
        "\n",
        "def ..."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PbWyuyAmwT1r"
      },
      "source": [
        "## study for each level"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "asJemX3dwQsH"
      },
      "source": [
        "### Analyse et distance de mahalanobis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92DENVqjwY8T"
      },
      "source": [
        "#mahalanobis distance :\n",
        "errs=anomaly_detect_mahalanobis(x_train_features=x_train_features, x_test_features=x_test_features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OPw2no7tFYz"
      },
      "source": [
        "#compute roc_auc_score: \n",
        "from sklea ...\n",
        "res_maha=roc_auc_score(y_test==0,errs)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}